twelve <- read.csv("C:/Users/farlc_000/Documents/2012.csv")
fourteen <- read.csv("C:/Users/farlc_000/Documents/2014.csv")
sixteen <- read.csv("C:/Users/farlc_000/Documents/2016.csv")
library(randomForest)
library(caret)
fit16 = randomForest(data = sixteen[,-(1:4)], DemocraticTwoPartyVoteshare ~.)
varImpPlot(fit16, type=2)
VI <- importance(fit16)
write.csv(VI, file = "VI16.csv", row.names = T)
sorted <- read.csv("VI16.csv")
barplot(sorted$IncNodePurity[1:15], horiz = T, names.arg = sorted$Var[1:15])
fit12 = randomForest(data = twelve[,-(1:4)], DemocraticTwoPartyVoteshare ~.)
varImpPlot(fit12, type=2)
VI <- importance(fit12)
write.csv(VI, file = "VI12.csv", row.names = T)
fit14 = randomForest(data = fourteen[,-(1:4)], DemocraticTwoPartyVoteshare ~.)
varImpPlot(fit14, type=2)
VI <- importance(fit14)
write.csv(VI, file = "VI14.csv", row.names = T)
data1416 <- rbind(fourteen, sixteen)
AllData <- rbind(twelve, data1416)
X <- split(AllData, AllData$X)
cali <- X[[1]]
penn <- X[[2]]
tx <- X[[3]]
wash <- X[[4]]
fitcali <- randomForest(data = cali[,-(1:4)], DemocraticTwoPartyVoteshare ~.)
varImpPlot(fitcali, type = 2)
fitwash <- randomForest(data = wash[,-(1:4)], DemocraticTwoPartyVoteshare ~.)
varImpPlot(fitwash, type = 2)
fittx <- randomForest(data = tx[,-(1:4)], DemocraticTwoPartyVoteshare ~.)
varImpPlot(fittx, type = 2)
fitpenn <- randomForest(data = penn[,-(1:4)], DemocraticTwoPartyVoteshare ~.)
varImpPlot(fitpenn, type = 2)
VI <- importance(fitpenn)
write.csv(VI, file = "VIPA.csv", row.names = T)
VI <- importance(fittx)
write.csv(VI, file = "VITX.csv", row.names = T)
VI <- importance(fitwash)
write.csv(VI, file = "VIWA.csv", row.names = T)
VI <- importance(fitcali)
write.csv(VI, file = "VICA.csv", row.names = T)
WAneighbor <- read.csv("~/Senior Year/Senior Design/Washington Neighbors.csv", header = T, row.names = 1)
PAneighbor <- read.csv("~/Senior Year/Senior Design/Pennsylvania Neighbors.csv", header = T, row.names = 1)
TXneighbor <- read.csv("~/Senior Year/Senior Design/Texas Neighbors v2.csv", header = T, row.names = 1)
CAneighbor <- read.csv("~/Senior Year/Senior Design/California Neighbors.csv", header = T, row.names = 1)
cali$DistrictNum <- cali$GEO.id2 - 600
wash$DistrictNum <- wash$GEO.id2 - 5300
penn$DistrictNum <- penn$GEO.id2 - 4200
tx$DistrictNum <- tx$GEO.id2 - 4800
cali2012 <- cali[(1:53),]
wash2012 <- wash[(1:10),]
penn2012 <- penn[(1:18),]
tx2012 <- tx[(1:36),]
neighbordifference <- function(district, demo, neighbor){ #assumes both are vectors of equal size
n <- sum(neighbor)
nbrsdem <- demo[which(neighbor)]
differences <- abs(demo[district] - nbrsdem)
neighbordifference <- sum(differences) / n
}
num_districts <- length(cali2012$GEO.id)
agdifferencesCA <- rep(NA, num_districts)
vetdifferencesCA <- rep(NA, num_districts)
blackdifferencesCA <- rep(NA, num_districts)
incomedifferencesCA <- rep(NA, num_districts)
foreigndifferencesCA <- rep(NA, num_districts)
unempdifferencesCA <- rep(NA, num_districts)
youngdifferencesCA <- rep(NA, num_districts)
somecollegedifferencesCA <- rep(NA, num_districts)
whitedifferencesCA <- rep(NA, num_districts)
tworacedifferencesCA <- rep(NA, num_districts)
elderlydifferencesCA <- rep(NA, num_districts)
tradedifferencesCA <- rep(NA, num_districts)
englishdifferencesCA <- rep(NA, num_districts)
marrieddifferencesCA <- rep(NA, num_districts)
hispanicdifferencesCA <- rep(NA, num_districts)
enrolleddifferencesCA <- rep(NA, num_districts)
for (i in 1:num_districts){
agdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_EC50, unname(CAneighbor[,i]))
vetdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC101, unname(CAneighbor[,i]))
blackdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_DH95, unname(CAneighbor[,i]))
incomedifferencesCA[i] <- neighbordifference(i, cali2012$HC01_EC118, unname(CAneighbor[,i]))
foreigndifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC140, unname(CAneighbor[,i]))
unempdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_EC12, unname(CAneighbor[,i]))
youngdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_EST_ASB02, unname(CAneighbor[,i]))
somecollegedifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC89, unname(CAneighbor[,i]))
whitedifferencesCA[i] <- neighbordifference(i, cali2012$HC03_DH94, unname(CAneighbor[,i]))
tworacedifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC101, unname(CAneighbor[,i]))
elderlydifferencesCA[i] <- neighbordifference(i, cali2012$HC03_DH102, unname(CAneighbor[,i]))
tradedifferencesCA[i] <- neighbordifference(i, cali2012$HC03_EC53, unname(CAneighbor[,i]))
englishdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC171, unname(CAneighbor[,i]))
marrieddifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC45, unname(CAneighbor[,i]))
hispanicdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_DH88, unname(CAneighbor[,i]))
enrolleddifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC81, unname(CAneighbor[,i]))
}
num_districts <- length(wash2012$DistrictNum)
agdifferencesWA <- rep(NA, num_districts)
vetdifferencesWA <- rep(NA, num_districts)
blackdifferencesWA <- rep(NA, num_districts)
incomedifferencesWA <- rep(NA, num_districts)
foreigndifferencesWA <- rep(NA, num_districts)
unempdifferencesWA <- rep(NA, num_districts)
youngdifferencesWA <- rep(NA, num_districts)
somecollegedifferencesWA <- rep(NA, num_districts)
whitedifferencesWA <- rep(NA, num_districts)
tworacedifferencesWA <- rep(NA, num_districts)
elderlydifferencesWA <- rep(NA, num_districts)
tradedifferencesWA <- rep(NA, num_districts)
englishdifferencesWA <- rep(NA, num_districts)
marrieddifferencesWA <- rep(NA, num_districts)
hispanicdifferencesWA <- rep(NA, num_districts)
enrolleddifferencesWA <- rep(NA, num_districts)
for (i in 1:num_districts){
agdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_EC50, unname(WAneighbor[,i]))
vetdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC101, unname(WAneighbor[,i]))
blackdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_DH95, unname(WAneighbor[,i]))
incomedifferencesWA[i] <- neighbordifference(i, wash2012$HC01_EC118, unname(WAneighbor[,i]))
foreigndifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC140, unname(WAneighbor[,i]))
unempdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_EC12, unname(WAneighbor[,i]))
youngdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_EST_ASB02, unname(WAneighbor[,i]))
somecollegedifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC89, unname(WAneighbor[,i]))
whitedifferencesWA[i] <- neighbordifference(i, wash2012$HC03_DH94, unname(WAneighbor[,i]))
tworacedifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC101, unname(WAneighbor[,i]))
elderlydifferencesWA[i] <- neighbordifference(i, wash2012$HC03_DH102, unname(WAneighbor[,i]))
tradedifferencesWA[i] <- neighbordifference(i, wash2012$HC03_EC53, unname(WAneighbor[,i]))
englishdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC171, unname(WAneighbor[,i]))
marrieddifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC45, unname(WAneighbor[,i]))
hispanicdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_DH88, unname(WAneighbor[,i]))
enrolleddifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC81, unname(WAneighbor[,i]))
}
num_districts <- length(penn2012$DistrictNum)
agdifferencesPA <- rep(NA, num_districts)
vetdifferencesPA <- rep(NA, num_districts)
blackdifferencesPA <- rep(NA, num_districts)
incomedifferencesPA <- rep(NA, num_districts)
foreigndifferencesPA <- rep(NA, num_districts)
unempdifferencesPA <- rep(NA, num_districts)
youngdifferencesPA <- rep(NA, num_districts)
somecollegedifferencesPA <- rep(NA, num_districts)
whitedifferencesPA <- rep(NA, num_districts)
tworacedifferencesPA <- rep(NA, num_districts)
elderlydifferencesPA <- rep(NA, num_districts)
tradedifferencesPA <- rep(NA, num_districts)
englishdifferencesPA <- rep(NA, num_districts)
marrieddifferencesPA <- rep(NA, num_districts)
hispanicdifferencesPA <- rep(NA, num_districts)
enrolleddifferencesPA <- rep(NA, num_districts)
for (i in 1:num_districts){
agdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_EC50, unname(PAneighbor[,i]))
vetdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC101, unname(PAneighbor[,i]))
blackdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_DH95, unname(PAneighbor[,i]))
incomedifferencesPA[i] <- neighbordifference(i, penn2012$HC01_EC118, unname(PAneighbor[,i]))
foreigndifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC140, unname(PAneighbor[,i]))
unempdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_EC12, unname(PAneighbor[,i]))
youngdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_EST_ASB02, unname(PAneighbor[,i]))
somecollegedifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC89, unname(PAneighbor[,i]))
whitedifferencesPA[i] <- neighbordifference(i, penn2012$HC03_DH94, unname(PAneighbor[,i]))
tworacedifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC101, unname(PAneighbor[,i]))
elderlydifferencesPA[i] <- neighbordifference(i, penn2012$HC03_DH102, unname(PAneighbor[,i]))
tradedifferencesPA[i] <- neighbordifference(i, penn2012$HC03_EC53, unname(PAneighbor[,i]))
englishdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC171, unname(PAneighbor[,i]))
marrieddifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC45, unname(PAneighbor[,i]))
hispanicdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_DH88, unname(PAneighbor[,i]))
enrolleddifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC81, unname(PAneighbor[,i]))
}
num_districts <- length(tx2012$DistrictNum)
agdifferencesTX <- rep(NA, num_districts)
vetdifferencesTX <- rep(NA, num_districts)
blackdifferencesTX <- rep(NA, num_districts)
incomedifferencesTX <- rep(NA, num_districts)
foreigndifferencesTX <- rep(NA, num_districts)
unempdifferencesTX <- rep(NA, num_districts)
youngdifferencesTX <- rep(NA, num_districts)
somecollegedifferencesTX <- rep(NA, num_districts)
whitedifferencesTX <- rep(NA, num_districts)
tworacedifferencesTX <- rep(NA, num_districts)
elderlydifferencesTX <- rep(NA, num_districts)
tradedifferencesTX <- rep(NA, num_districts)
englishdifferencesTX <- rep(NA, num_districts)
marrieddifferencesTX <- rep(NA, num_districts)
hispanicdifferencesTX <- rep(NA, num_districts)
enrolleddifferencesTX <- rep(NA, num_districts)
for (i in 1:num_districts){
agdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_EC50, unname(TXneighbor[,i]))
vetdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC101, unname(TXneighbor[,i]))
blackdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_DH95, unname(TXneighbor[,i]))
incomedifferencesTX[i] <- neighbordifference(i, tx2012$HC01_EC118, unname(TXneighbor[,i]))
foreigndifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC140, unname(TXneighbor[,i]))
unempdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_EC12, unname(TXneighbor[,i]))
youngdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_EST_ASB02, unname(TXneighbor[,i]))
somecollegedifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC89, unname(TXneighbor[,i]))
whitedifferencesTX[i] <- neighbordifference(i, tx2012$HC03_DH94, unname(TXneighbor[,i]))
tworacedifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC101, unname(TXneighbor[,i]))
elderlydifferencesTX[i] <- neighbordifference(i, tx2012$HC03_DH102, unname(TXneighbor[,i]))
tradedifferencesTX[i] <- neighbordifference(i, tx2012$HC03_EC53, unname(TXneighbor[,i]))
englishdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC171, unname(TXneighbor[,i]))
marrieddifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC45, unname(TXneighbor[,i]))
hispanicdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_DH88, unname(TXneighbor[,i]))
enrolleddifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC81, unname(TXneighbor[,i]))
}
agconfCA <- t.test(agdifferencesCA, conf.level = 0.95)$conf.int
agconfWA <- t.test(agdifferencesWA, conf.level = 0.95)$conf.int
agconfPA <- t.test(agdifferencesPA, conf.level = 0.95)$conf.int
agconfTX <- t.test(agdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(agdifferencesCA), mean(agdifferencesWA), mean(agdifferencesPA), mean(agdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Percentage of Agriculture Workers", 4)
lowerconf <- c(agconfCA[1], agconfWA[1], agconfPA[1], agconfTX[1])
upperconf <- c(agconfCA[2], agconfWA[2], agconfPA[2], agconfTX[2])
agdf <- data.frame(states, means, lowerconf, upperconf, label)
vetconfCA <- t.test(vetdifferencesCA, conf.level = 0.95)$conf.int
vetconfWA <- t.test(vetdifferencesWA, conf.level = 0.95)$conf.int
vetconfPA <- t.test(vetdifferencesPA, conf.level = 0.95)$conf.int
vetconfTX <- t.test(vetdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(vetdifferencesCA), mean(vetdifferencesWA), mean(vetdifferencesPA), mean(vetdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Percentage of Veterans", 4)
lowerconf <- c(vetconfCA[1], vetconfWA[1], vetconfPA[1], vetconfTX[1])
upperconf <- c(vetconfCA[2], vetconfWA[2], vetconfPA[2], vetconfTX[2])
vetdf <- data.frame(states, means, lowerconf, upperconf, label)
blackconfCA <- t.test(blackdifferencesCA, conf.level = 0.95)$conf.int
blackconfWA <- t.test(blackdifferencesWA, conf.level = 0.95)$conf.int
blackconfPA <- t.test(blackdifferencesPA, conf.level = 0.95)$conf.int
blackconfTX <- t.test(blackdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(blackdifferencesCA), mean(blackdifferencesWA), mean(blackdifferencesPA), mean(blackdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Population Black", 4)
lowerconf <- c(blackconfCA[1], blackconfWA[1], blackconfPA[1], blackconfTX[1])
upperconf <- c(blackconfCA[2], blackconfWA[2], blackconfPA[2], blackconfTX[2])
blackdf <- data.frame(states, means, lowerconf, upperconf, label)
incomeconfCA <- t.test(incomedifferencesCA, conf.level = 0.95)$conf.int
incomeconfWA <- t.test(incomedifferencesWA, conf.level = 0.95)$conf.int
incomeconfPA <- t.test(incomedifferencesPA, conf.level = 0.95)$conf.int
incomeconfTX <- t.test(incomedifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(incomedifferencesCA), mean(incomedifferencesWA), mean(incomedifferencesPA), mean(incomedifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Per Capita Income Difference", 4)
lowerconf <- c(incomeconfCA[1], incomeconfWA[1], incomeconfPA[1], incomeconfTX[1])
upperconf <- c(incomeconfCA[2], incomeconfWA[2], incomeconfPA[2], incomeconfTX[2])
incomedf <- data.frame(states, means, lowerconf, upperconf, label)
foreignconfCA <- t.test(foreigndifferencesCA, conf.level = 0.95)$conf.int
foreignconfWA <- t.test(foreigndifferencesWA, conf.level = 0.95)$conf.int
foreignconfPA <- t.test(foreigndifferencesPA, conf.level = 0.95)$conf.int
foreignconfTX <- t.test(foreigndifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(foreigndifferencesCA), mean(foreigndifferencesWA), mean(foreigndifferencesPA), mean(foreigndifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Foreign Born", 4)
lowerconf <- c(foreignconfCA[1], foreignconfWA[1], foreignconfPA[1], foreignconfTX[1])
upperconf <- c(foreignconfCA[2], foreignconfWA[2], foreignconfPA[2], foreignconfTX[2])
foreigndf <- data.frame(states, means, lowerconf, upperconf, label)
unempconfCA <- t.test(unempdifferencesCA, conf.level = 0.95)$conf.int
unempconfWA <- t.test(unempdifferencesWA, conf.level = 0.95)$conf.int
unempconfPA <- t.test(unempdifferencesPA, conf.level = 0.95)$conf.int
unempconfTX <- t.test(unempdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(unempdifferencesCA), mean(unempdifferencesWA), mean(unempdifferencesPA), mean(unempdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Unemployment Rate", 4)
lowerconf <- c(unempconfCA[1], unempconfWA[1], unempconfPA[1], unempconfTX[1])
upperconf <- c(unempconfCA[2], unempconfWA[2], unempconfPA[2], unempconfTX[2])
unempdf <- data.frame(states, means, lowerconf, upperconf, label)
youngconfCA <- t.test(youngdifferencesCA, conf.level = 0.95)$conf.int
youngconfWA <- t.test(youngdifferencesWA, conf.level = 0.95)$conf.int
youngconfPA <- t.test(youngdifferencesPA, conf.level = 0.95)$conf.int
youngconfTX <- t.test(youngdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(youngdifferencesCA), mean(youngdifferencesWA), mean(youngdifferencesPA), mean(youngdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Aged 20-34", 4)
lowerconf <- c(youngconfCA[1], youngconfWA[1], youngconfPA[1], youngconfTX[1])
upperconf <- c(youngconfCA[2], youngconfWA[2], youngconfPA[2], youngconfTX[2])
youngdf <- data.frame(states, means, lowerconf, upperconf, label)
somecollegeconfCA <- t.test(somecollegedifferencesCA, conf.level = 0.95)$conf.int
somecollegeconfWA <- t.test(somecollegedifferencesWA, conf.level = 0.95)$conf.int
somecollegeconfPA <- t.test(somecollegedifferencesPA, conf.level = 0.95)$conf.int
somecollegeconfTX <- t.test(somecollegedifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(somecollegedifferencesCA), mean(somecollegedifferencesWA), mean(somecollegedifferencesPA), mean(somecollegedifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage with Some College but No Degree", 4)
lowerconf <- c(somecollegeconfCA[1], somecollegeconfWA[1], somecollegeconfPA[1], somecollegeconfTX[1])
upperconf <- c(somecollegeconfCA[2], somecollegeconfWA[2], somecollegeconfPA[2], somecollegeconfTX[2])
somecollegedf <- data.frame(states, means, lowerconf, upperconf, label)
whiteconfCA <- t.test(whitedifferencesCA, conf.level = 0.95)$conf.int
whiteconfWA <- t.test(whitedifferencesWA, conf.level = 0.95)$conf.int
whiteconfPA <- t.test(whitedifferencesPA, conf.level = 0.95)$conf.int
whiteconfTX <- t.test(whitedifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(whitedifferencesCA), mean(whitedifferencesWA), mean(whitedifferencesPA), mean(whitedifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage White", 4)
lowerconf <- c(whiteconfCA[1], whiteconfWA[1], whiteconfPA[1], whiteconfTX[1])
upperconf <- c(whiteconfCA[2], whiteconfWA[2], whiteconfPA[2], whiteconfTX[2])
whitedf <- data.frame(states, means, lowerconf, upperconf, label)
tworaceconfCA <- t.test(tworacedifferencesCA, conf.level = 0.95)$conf.int
tworaceconfWA <- t.test(tworacedifferencesWA, conf.level = 0.95)$conf.int
tworaceconfPA <- t.test(tworacedifferencesPA, conf.level = 0.95)$conf.int
tworaceconfTX <- t.test(tworacedifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(tworacedifferencesCA), mean(tworacedifferencesWA), mean(tworacedifferencesPA), mean(tworacedifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Biracial", 4)
lowerconf <- c(tworaceconfCA[1], tworaceconfWA[1], tworaceconfPA[1], tworaceconfTX[1])
upperconf <- c(tworaceconfCA[2], tworaceconfWA[2], tworaceconfPA[2], tworaceconfTX[2])
tworacedf <- data.frame(states, means, lowerconf, upperconf, label)
elderlyconfCA <- t.test(elderlydifferencesCA, conf.level = 0.95)$conf.int
elderlyconfWA <- t.test(elderlydifferencesWA, conf.level = 0.95)$conf.int
elderlyconfPA <- t.test(elderlydifferencesPA, conf.level = 0.95)$conf.int
elderlyconfTX <- t.test(elderlydifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(elderlydifferencesCA), mean(elderlydifferencesWA), mean(elderlydifferencesPA), mean(elderlydifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage of Elderly Under the Poverty Line", 4)
lowerconf <- c(elderlyconfCA[1], elderlyconfWA[1], elderlyconfPA[1], elderlyconfTX[1])
upperconf <- c(elderlyconfCA[2], elderlyconfWA[2], elderlyconfPA[2], elderlyconfTX[2])
elderlydf <- data.frame(states, means, lowerconf, upperconf, label)
tradeconfCA <- t.test(tradedifferencesCA, conf.level = 0.95)$conf.int
tradeconfWA <- t.test(tradedifferencesWA, conf.level = 0.95)$conf.int
tradeconfPA <- t.test(tradedifferencesPA, conf.level = 0.95)$conf.int
tradeconfTX <- t.test(tradedifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(tradedifferencesCA), mean(tradedifferencesWA), mean(tradedifferencesPA), mean(tradedifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Employed in Wholesale Trade", 4)
lowerconf <- c(tradeconfCA[1], tradeconfWA[1], tradeconfPA[1], tradeconfTX[1])
upperconf <- c(tradeconfCA[2], tradeconfWA[2], tradeconfPA[2], tradeconfTX[2])
tradedf <- data.frame(states, means, lowerconf, upperconf, label)
englishconfCA <- t.test(englishdifferencesCA, conf.level = 0.95)$conf.int
englishconfWA <- t.test(englishdifferencesWA, conf.level = 0.95)$conf.int
englishconfPA <- t.test(englishdifferencesPA, conf.level = 0.95)$conf.int
englishconfTX <- t.test(englishdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(englishdifferencesCA), mean(englishdifferencesWA), mean(englishdifferencesPA), mean(englishdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Percentage Speaking English at Home Difference", 4)
lowerconf <- c(englishconfCA[1], englishconfWA[1], englishconfPA[1], englishconfTX[1])
upperconf <- c(englishconfCA[2], englishconfWA[2], englishconfPA[2], englishconfTX[2])
englishdf <- data.frame(states, means, lowerconf, upperconf, label)
marriedconfCA <- t.test(marrieddifferencesCA, conf.level = 0.95)$conf.int
marriedconfWA <- t.test(marrieddifferencesWA, conf.level = 0.95)$conf.int
marriedconfPA <- t.test(marrieddifferencesPA, conf.level = 0.95)$conf.int
marriedconfTX <- t.test(marrieddifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(marrieddifferencesCA), mean(marrieddifferencesWA), mean(marrieddifferencesPA), mean(marrieddifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Percentage of Married Women Difference", 4)
lowerconf <- c(marriedconfCA[1], marriedconfWA[1], marriedconfPA[1], marriedconfTX[1])
upperconf <- c(marriedconfCA[2], marriedconfWA[2], marriedconfPA[2], marriedconfTX[2])
marrieddf <- data.frame(states, means, lowerconf, upperconf, label)
hispanicconfCA <- t.test(hispanicdifferencesCA, conf.level = 0.95)$conf.int
hispanicconfWA <- t.test(hispanicdifferencesWA, conf.level = 0.95)$conf.int
hispanicconfPA <- t.test(hispanicdifferencesPA, conf.level = 0.95)$conf.int
hispanicconfTX <- t.test(hispanicdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(hispanicdifferencesCA), mean(hispanicdifferencesWA), mean(hispanicdifferencesPA), mean(hispanicdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Population Hispanic", 4)
lowerconf <- c(hispanicconfCA[1], hispanicconfWA[1], hispanicconfPA[1], hispanicconfTX[1])
upperconf <- c(hispanicconfCA[2], hispanicconfWA[2], hispanicconfPA[2], hispanicconfTX[2])
hispanicdf <- data.frame(states, means, lowerconf, upperconf, label)
enrolledconfCA <- t.test(enrolleddifferencesCA, conf.level = 0.95)$conf.int
enrolledconfWA <- t.test(enrolleddifferencesWA, conf.level = 0.95)$conf.int
enrolledconfPA <- t.test(enrolleddifferencesPA, conf.level = 0.95)$conf.int
enrolledconfTX <- t.test(enrolleddifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(enrolleddifferencesCA), mean(enrolleddifferencesWA), mean(enrolleddifferencesPA), mean(enrolleddifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Percent Enrolled in College Difference", 4)
lowerconf <- c(enrolledconfCA[1], enrolledconfWA[1], enrolledconfPA[1], enrolledconfTX[1])
upperconf <- c(enrolledconfCA[2], enrolledconfWA[2], enrolledconfPA[2], enrolledconfTX[2])
enrolleddf <- data.frame(states, means, lowerconf, upperconf, label)
ggplot(vetdf, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1))
vetag <- rbind(agdf, vetdf)
ggplot(vetag, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1))
ggplot(agdf, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1))
ggplot(blackdf, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1))
ggplot(incomedf, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1))
plot1 <- do.call("rbind", list(blackdf, agdf, vetdf))
plot2 <- do.call("rbind", list(unempdf, youngdf, somecollegedf, whitedf))
plot3 <- do.call("rbind", list(tworacedf, elderlydf, tradedf, englishdf))
plot4 <- do.call("rbind", list(marrieddf, hispanicdf, enrolleddf))
ggplot(plot1, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
geom_hline(yintercept = 0)

ggplot(plot2, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
geom_hline(yintercept = 0)

ggplot(plot3, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
geom_hline(yintercept = 0)

ggplot(plot4, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
geom_hline(yintercept = 0)

englishAllDistricts <- c(englishdifferencesCA, englishdifferencesPA, englishdifferencesTX, englishdifferencesWA)
allStates <- c(rep("California",53), rep("Pennsylvania",18), rep("Texas",36), rep("Washington", 10))
district <- c(cali2012$DistrictNum, penn2012$DistrictNum, tx2012$DistrictNum, wash2012$DistrictNum)
allCommission <- c(rep("Independent", 63), rep("Partisan", 54))
englishBoxDF <- data.frame(englishAllDistricts, allStates, district)
check_outlier <- function(v, coef=1.51){
quantiles <- quantile(v,probs=c(0.25,0.75))
IQR <- quantiles[2]-quantiles[1]
res <- v < (quantiles[1]-coef*IQR)|v > (quantiles[2]+coef*IQR)
return(res)
}
library(dplyr)
commissionEnglish <- englishBoxDF %>%
mutate(commission = ifelse(allStates == "California", "Independent", ifelse(allStates == "Washington", "Independent", "Partisan")))
independentEnglish <- commissionEnglish %>%
filter(commission == "Independent") %>%
mutate(outliers = ifelse(check_outlier(englishAllDistricts), paste(allStates, as.character(district), sep = " "), ""))
partisanEnglish <- commissionEnglish %>%
filter(commission == "Partisan") %>%
mutate(outliers = ifelse(check_outlier(englishAllDistricts), paste(allStates, as.character(district), sep = " "), ""))
outlierEnglish <- rbind(independentEnglish, partisanEnglish)
library(ggrepel)
ggplot(data = outlierEnglish, aes(x = commission, y = englishAllDistricts, color = commission)) +
geom_boxplot() + ggtitle("Absolute Difference of Percentage Speaking English at Home") +
theme(plot.title = element_text(hjust = 0.5)) + geom_text_repel(aes(label=outliers))

plot5 <- do.call("rbind", list(whitedf,blackdf, hispanicdf, tworacedf))
plot6 <- do.call("rbind", list(unempdf, elderlydf, tradedf, agdf))
plot7 <- do.call("rbind", list(enrolleddf, somecollegedf, vetdf))
plot8 <- do.call("rbind", list(youngdf, marrieddf, englishdf))
ggplot(plot5, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
geom_hline(yintercept = 0) + ggtitle("Race Differences by State") + ylab("Absolute Difference") +
xlab("") + theme(plot.title = element_text(hjust = 0.5))

ggplot(plot6, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
geom_hline(yintercept = 0) + ggtitle("Race Differences by State") + ylab("Absolute Difference") +
xlab("") + theme(plot.title = element_text(hjust = 0.5))

ggplot(plot7, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
geom_hline(yintercept = 0) + ggtitle("Educational Congressional District Neighbor Differences by State") + ylab("Absolute Difference") +
xlab("") + theme(plot.title = element_text(hjust = 0.5))

ggplot(plot8, aes(x = label, y = means, color = states)) +
geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
geom_hline(yintercept = 0) + ggtitle("Social Statistic Congressional District Neighbor Differences by State") + ylab("Absolute Difference") +
xlab("") + theme(plot.title = element_text(hjust = 0.5))

whiteAllDistricts <- c(whitedifferencesCA, whitedifferencesWA, whitedifferencesTX, whitedifferencesPA)
blackAllDistricts <- c(blackdifferencesCA, blackdifferencesWA, blackdifferencesTX, blackdifferencesPA)
hispanicAllDistricts <- c(hispanicdifferencesCA, hispanicdifferencesWA, hispanicdifferencesTX, hispanicdifferencesPA)
tworaceAllDistricts <- c(tworacedifferencesCA, tworacedifferencesWA, tworacedifferencesTX, tworacedifferencesPA)
labels <- c(rep("Percentage White Difference", 117), rep("Percentage Black Difference", 117), rep("Percentage Hispanic Difference", 117),
rep("Percentage Biracial Difference",117))
raceData <- c(whiteAllDistricts, blackAllDistricts, hispanicAllDistricts, tworaceAllDistricts)
allStates <- rep(c(rep("California",53), rep("Washington",10), rep("Texas",36), rep("Pennsylvania", 18)),4)
allCommission <- rep(c(rep("Independent", 63), rep("Partisan", 54)),4)
district <- rep(c(cali2012$DistrictNum, wash2012$DistrictNum, tx2012$DistrictNum, penn2012$DistrictNum),4)
raceBoxDF <- data.frame(raceData, allStates, allCommission, district, labels)
unempAllDistricts <- c(unempdifferencesCA, unempdifferencesWA, unempdifferencesTX, unempdifferencesPA)
elderlyAllDistricts <- c(elderlydifferencesCA, elderlydifferencesWA, elderlydifferencesTX, elderlydifferencesPA)
tradeAllDistricts <- c(tradedifferencesCA, tradedifferencesWA, tradedifferencesTX, tradedifferencesPA)
agAllDistricts <- c(agdifferencesCA, agdifferencesWA, agdifferencesTX, agdifferencesPA)
enrolledAllDistricts <- c(enrolleddifferencesCA, enrolleddifferencesWA, enrolleddifferencesTX, enrolleddifferencesPA)
somecollegeAllDistricts <- c(somecollegedifferencesCA, somecollegedifferencesWA, somecollegedifferencesTX, somecollegedifferencesPA)
vetAllDistricts <- c(vetdifferencesCA, vetdifferencesWA, vetdifferencesTX, vetdifferencesPA)
youngAllDistricts <- c(youngdifferencesCA, youngdifferencesWA, youngdifferencesTX, youngdifferencesPA)
marriedAllDistricts <- c(marrieddifferencesCA, marrieddifferencesWA, marrieddifferencesTX, marrieddifferencesPA)
foreignAllDistricts <- c(foreigndifferencesCA, foreigndifferencesWA, foreigndifferencesTX, foreigndifferencesPA)
incomeAllDistricts <- c(incomedifferencesCA, incomedifferencesWA, incomedifferencesTX, incomedifferencesPA)
allData <- c(raceData, unempAllDistricts, elderlyAllDistricts, tradeAllDistricts, agAllDistricts, enrolledAllDistricts,
somecollegeAllDistricts, vetAllDistricts, youngAllDistricts, marriedAllDistricts, englishAllDistricts,
foreignAllDistricts, incomeAllDistricts)
labels <- c(rep("Percentage White Difference", 117),
rep("Percentage Black Difference", 117),
rep("Percentage Hispanic Difference", 117),
rep("Percentage Biracial Difference",117),
rep("Unemployment Rate Difference",117),
rep("Percent Poor Elderly Difference", 117),
rep("Percent Employed in Wholesale Trade Difference", 117),
rep("Percent Employed in Agriculture Difference", 117),
rep("Percent Enrolled in College Difference", 117),
rep("Percent College Dropout Difference", 117),
rep("Veteran Population Difference", 117),
rep("Percent Age 20-34 Difference",117),
rep("Married Female Population Difference", 117),
rep("Percent Speak English at Home Difference",117),
rep("Foreign Born Difference",117),
rep("Per Capita Income Difference", 117))
allStates <- rep(c(rep("California",53), rep("Washington",10), rep("Texas",36), rep("Pennsylvania", 18)),16)
allCommission <- rep(c(rep("Independent", 63), rep("Partisan", 54)),16)
district <- rep(c(cali2012$DistrictNum, wash2012$DistrictNum, tx2012$DistrictNum, penn2012$DistrictNum),16)
allBoxDF <- data.frame(allData, allStates, allCommission, district, labels)
is_outlier <- function(x) {
return(x < quantile(x, 0.25) - 1.5 * IQR(x) | x > quantile(x, 0.75) + 1.5 * IQR(x))
}
raceBoxDF %>%
group_by(labels, allCommission) %>%
mutate(outlier = ifelse(is_outlier(raceData), paste(allStates, as.character(district), sep = " "), "")) %>%
ggplot(., aes(x = labels, y = raceData, color = allCommission)) +
geom_boxplot() + geom_text(aes(label = outlier))

ggplot(raceBoxDF, aes(x = labels, y = raceData, color = allCommission)) +
geom_boxplot() + ggtitle("Race Differences by State") + ylab("Absolute Difference") +
xlab("") + theme(plot.title = element_text(hjust = 0.5))

allOutlier <- allBoxDF %>%
group_by(labels, allCommission) %>%
mutate(outlier = ifelse(is_outlier(allData), paste(allStates, as.character(district), sep = " "), ""))
ggplot(allOutlier, aes(x = labels, y = allData)) + geom_boxplot(aes(fill = allCommission)) +
ggtitle("Demographic Congressional District Differences by District Drawing Method") + ylab("Absolute Difference") +
xlab("") + theme(plot.title = element_text(hjust = 0.5)) + #geom_text_repel(aes(label = outlier)) +
facet_wrap(~ labels, scales = "free")

ggplot(allOutlier, aes(x = labels, y = allData)) + geom_boxplot(aes(fill = allCommission)) +
ggtitle("Demographic Congressional District Differences by District Drawing Method") + ylab("Absolute Difference") +
xlab("") + theme(plot.title = element_text(hjust = 0.5)) +
facet_wrap(~ labels, scales = "free")
onlyOutliers <- filter(allOutlier, outlier != "")
ggplot(onlyOutliers, aes(allStates)) + geom_bar()

outlierDistrictCount <- onlyOutliers %>%
group_by(outlier) %>%
summarise(n = n()) %>%
filter(n > 1) %>%
mutate(state= gsub('[[:digit:]]+', '', outlier))
ggplot(outlierDistrictCount, aes(outlier)) +
geom_bar(stat = 'identity', aes(y = n, fill=state)) +
ggtitle("Number of Variables District is an Outlier On") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), plot.title = element_text(hjust = 0.5)) +
xlab("District") + ylab("Number of Outliers")

NA
totalOutlierCount <- allBoxDF %>%
group_by(labels, allStates) %>%
mutate(outlier = ifelse(is_outlier(allData), paste(allStates, as.character(district), sep = " "), "")) %>%
ungroup() %>%
filter(outlier != "") %>%
group_by(outlier) %>%
summarise(n = n()) %>%
filter(n > 3) %>%
mutate(state= gsub('[[:digit:]]+', '', outlier))
ggplot(totalOutlierCount, aes(outlier)) +
geom_bar(stat = 'identity', aes(y = n, fill=state)) +
ggtitle("Number of Variables District is an Outlier On (> 3)") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), plot.title = element_text(hjust = 0.5)) +
xlab("District") + ylab("Number of Outliers")

stateOutlierProp <- allBoxDF %>%
group_by(labels, allCommission) %>%
mutate(outlier = ifelse(is_outlier(allData), paste(allStates, as.character(district), sep = " "), "")) %>%
summarise(percent_outlier = ((length(unique(outlier))-1)/n()))
ggplot(stateOutlierProp, aes(allCommission)) +
geom_bar(stat = 'identity', aes(y = percent_outlier, fill=allCommission)) +
ggtitle("Proportion of Districts are Outliers by District Drawing Commission") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), plot.title = element_text(hjust = 0.5)) +
xlab("District") + ylab("Number of Outliers") + facet_wrap(~ labels, scales = "free")
Error: `predicate` must be a closure or function pointer
pennOutlierCount <- allOutlier %>%
filter(allStates == "Pennsylvania") %>%
group_by(district) %>%
summarise(times_outlier = sum(ifelse(outlier != "",1,0)))
outlier_pa <- tibble(DISTRICT=as.character(1:18),
Outliers = pennOutlierCount$times_outlier)
cd114_pa <- cd114_pa %>% left_join(outlier_pa, by="DISTRICT")
cd114_pa
Simple feature collection with 18 features and 2 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: -80.51949 ymin: 39.7198 xmax: -74.68952 ymax: 42.26986
epsg (SRID): 4269
proj4string: +proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs
First 10 features:
DISTRICT Outliers geometry
1 1 5 MULTIPOLYGON (((-75.4183 39...
2 2 6 MULTIPOLYGON (((-75.34917 4...
3 3 0 MULTIPOLYGON (((-79.76195 4...
4 4 0 MULTIPOLYGON (((-77.47109 3...
5 5 2 MULTIPOLYGON (((-80.26614 4...
6 6 0 MULTIPOLYGON (((-76.47761 4...
7 7 0 MULTIPOLYGON (((-76.18406 4...
8 8 0 MULTIPOLYGON (((-75.55788 4...
9 9 0 MULTIPOLYGON (((-80.11704 3...
10 10 0 MULTIPOLYGON (((-77.91393 4...
devtools::install_github("tidyverse/ggplot2")
require(ggplot2)
dm <- get_map(location="Washington", zoom=6)
Source : https://maps.googleapis.com/maps/api/staticmap?center=Washington&zoom=6&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Washington&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
geocode failed with status REQUEST_DENIED, location = "Washington"Error in data.frame(ll.lat = ll[1], ll.lon = ll[2], ur.lat = ur[1], ur.lon = ur[2]) :
arguments imply differing number of rows: 0, 1
map<-get_map(location = c(-77.1945,41.2033), zoom=7)
Source : https://maps.googleapis.com/maps/api/staticmap?center=41.2033,-77.1945&zoom=7&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
ggmap(map) +
geom_sf(data=cd114_pa,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

washOutlierCount <- allOutlier %>%
filter(allStates == "Washington") %>%
group_by(district) %>%
summarise(times_outlier = sum(ifelse(outlier != "",1,0)))
cd114_wa <- cd114 %>%
filter(STATENAME=="Washington") %>%
mutate(DISTRICT = as.character(DISTRICT)) %>%
select(DISTRICT)
outlier_wa <- tibble(DISTRICT=as.character(1:10),
Outliers = washOutlierCount$times_outlier)
cd114_wa <- cd114_wa %>% left_join(outlier_wa, by="DISTRICT")
map<-get_map(location = c(-120.7401,47.7511), zoom=6)
Source : https://maps.googleapis.com/maps/api/staticmap?center=47.7511,-120.7401&zoom=6&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
ggmap(map) +
geom_sf(data=cd114_wa,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

txOutlierCount <- allOutlier %>%
filter(allStates == "Texas") %>%
group_by(district) %>%
summarise(times_outlier = sum(ifelse(outlier != "",1,0)))
cd114_tx <- cd114 %>%
filter(STATENAME=="Texas") %>%
mutate(DISTRICT = as.character(DISTRICT)) %>%
select(DISTRICT)
outlier_tx <- tibble(DISTRICT=as.character(1:36),
Outliers = txOutlierCount$times_outlier)
cd114_tx <- cd114_tx %>% left_join(outlier_tx, by="DISTRICT")
map<-get_map(location = c(-99.9018,31.9689), zoom=6)
Source : https://maps.googleapis.com/maps/api/staticmap?center=31.9689,-99.9018&zoom=6&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
ggmap(map) +
geom_sf(data=cd114_tx,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

caOutlierCount <- allOutlier %>%
filter(allStates == "California") %>%
group_by(district) %>%
summarise(times_outlier = sum(ifelse(outlier != "",1,0)))
cd114_ca <- cd114 %>%
filter(STATENAME=="California") %>%
mutate(DISTRICT = as.character(DISTRICT)) %>%
select(DISTRICT)
outlier_ca <- tibble(DISTRICT=as.character(1:53),
Outliers = caOutlierCount$times_outlier)
cd114_ca <- cd114_ca %>% left_join(outlier_ca, by="DISTRICT")
map<-get_map(location = c(-119.4179,36.7783), zoom=6)
Source : https://maps.googleapis.com/maps/api/staticmap?center=36.7783,-119.4179&zoom=6&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
ggmap(map) +
geom_sf(data=cd114_ca,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

map_pa<-get_map(location = c(-77.1945,41.2033), zoom=7)
Source : https://maps.googleapis.com/maps/api/staticmap?center=41.2033,-77.1945&zoom=7&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
map_wa<-get_map(location = c(-120.7401,47.7511), zoom=6)
Source : https://maps.googleapis.com/maps/api/staticmap?center=47.7511,-120.7401&zoom=6&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
map_tx<-get_map(location = c(-99.9018,31.9689), zoom=6)
Source : https://maps.googleapis.com/maps/api/staticmap?center=31.9689,-99.9018&zoom=6&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
map_ca<-get_map(location = c(-119.4179,36.7783), zoom=6)
Source : https://maps.googleapis.com/maps/api/staticmap?center=36.7783,-119.4179&zoom=6&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
voteshare_pa <- tibble(DISTRICT=as.character(1:18),
`Average D Voteshare` = filter(avg_votes, State == "Pennsylvania")$Average)
cd114_vote_pa <- cd114_pa %>% left_join(voteshare_pa, by="DISTRICT")
ggmap(map_pa) +
geom_sf(data=cd114_vote_pa,aes(fill=Voteshare),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "red", high = "blue", limits=c(0,1)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

voteshare_wa <- tibble(DISTRICT=as.character(1:10),
`Average D Voteshare` = filter(avg_votes, State == "Washington")$Average)
cd114_vote_wa <- cd114_wa %>% left_join(voteshare_wa, by="DISTRICT")
ggmap(map_wa) +
geom_sf(data=cd114_vote_wa,aes(fill=Voteshare),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "red", high = "blue", limits=c(0,1)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

voteshare_ca <- tibble(DISTRICT=as.character(1:53),
`Average D Voteshare` = filter(avg_votes, State == "California")$Average)
cd114_vote_ca <- cd114_ca %>% left_join(voteshare_ca, by="DISTRICT")
ggmap(map_ca) +
geom_sf(data=cd114_vote_ca,aes(fill=Voteshare),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "red", high = "blue", limits=c(0,1)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

voteshare_tx <- tibble(DISTRICT=as.character(1:36),
`Average D Voteshare` = filter(avg_votes, State == "Texas")$Average)
cd114_vote_tx <- cd114_tx %>% left_join(voteshare_tx, by="DISTRICT")
ggmap(map_tx) +
geom_sf(data=cd114_vote_tx,aes(fill=`Average D Voteshare`),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "red", high = "blue", limits=c(0,1)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

map_philly <- get_map(c(-75.1652,39.9526), zoom=9)
Source : https://maps.googleapis.com/maps/api/staticmap?center=39.9526,-75.1652&zoom=9&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
ggmap(map_philly) +
geom_sf(data=cd114_pa,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

map_la <- get_map(c(-118.2437,34.0522), zoom=8)
Source : https://maps.googleapis.com/maps/api/staticmap?center=34.0522,-118.2437&zoom=8&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
ggmap(map_la) +
geom_sf(data=cd114_ca,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

map_dallas <- get_map(c(-96.7970,32.7767), zoom=7)
Source : https://maps.googleapis.com/maps/api/staticmap?center=32.7767,-96.797&zoom=7&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
ggmap(map_dallas) +
geom_sf(data=cd114_tx,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

map_hou <- get_map(c(-95.3698,29.7604), zoom=8)
Source : https://maps.googleapis.com/maps/api/staticmap?center=29.7604,-95.3698&zoom=8&size=640x640&scale=2&maptype=terrain&language=en-EN&key=AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA
ggmap(map_hou) +
geom_sf(data=cd114_tx,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) +
scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

---
title: "Senior Design"
output:
  pdf_document: default
  html_notebook: default
---


```{r}
twelve <- read.csv("C:/Users/farlc_000/Documents/2012.csv")
fourteen <- read.csv("C:/Users/farlc_000/Documents/2014.csv")
sixteen <- read.csv("C:/Users/farlc_000/Documents/2016.csv")
```


```{r}
library(randomForest)
library(caret)
```

```{r}
fit16 = randomForest(data = sixteen[,-(1:4)], DemocraticTwoPartyVoteshare ~.)

varImpPlot(fit16, type=2)
VI <- importance(fit16)
write.csv(VI, file = "VI16.csv", row.names = T)
``` 

```{r}
sorted <- read.csv("VI16.csv")
barplot(sorted$IncNodePurity[1:15], horiz = T, names.arg = sorted$Var[1:15])
```

```{r}
fit12 = randomForest(data = twelve[,-(1:4)], DemocraticTwoPartyVoteshare ~.)

varImpPlot(fit12, type=2)

VI <- importance(fit12)
write.csv(VI, file = "VI12.csv", row.names = T)
```

```{r}
fit14 = randomForest(data = fourteen[,-(1:4)], DemocraticTwoPartyVoteshare ~.)

varImpPlot(fit14, type=2)

VI <- importance(fit14)
write.csv(VI, file = "VI14.csv", row.names = T)
```

```{r}
data1416 <- rbind(fourteen, sixteen)
AllData <- rbind(twelve, data1416)
```

```{r}
X <- split(AllData, AllData$X)
cali <- X[[1]]
penn <- X[[2]]
tx <- X[[3]]
wash <- X[[4]]
```

```{r}

```

```{r}
fitcali <- randomForest(data = cali[,-(1:4)], DemocraticTwoPartyVoteshare ~.)

varImpPlot(fitcali, type = 2)
```

```{r}
fitwash <- randomForest(data = wash[,-(1:4)], DemocraticTwoPartyVoteshare ~.)

varImpPlot(fitwash, type = 2)
```

```{r}
fittx <- randomForest(data = tx[,-(1:4)], DemocraticTwoPartyVoteshare ~.)

varImpPlot(fittx, type = 2)
```

```{r}
fitpenn <- randomForest(data = penn[,-(1:4)], DemocraticTwoPartyVoteshare ~.)

varImpPlot(fitpenn, type = 2)
```

```{r}
VI <- importance(fitpenn)
write.csv(VI, file = "VIPA.csv", row.names = T)
VI <- importance(fittx)
write.csv(VI, file = "VITX.csv", row.names = T)
VI <- importance(fitwash)
write.csv(VI, file = "VIWA.csv", row.names = T)
VI <- importance(fitcali)
write.csv(VI, file = "VICA.csv", row.names = T)
```

```{r}
WAneighbor <- read.csv("~/Senior Year/Senior Design/Washington Neighbors.csv", header = T, row.names = 1)
PAneighbor <- read.csv("~/Senior Year/Senior Design/Pennsylvania Neighbors.csv", header = T, row.names = 1)
TXneighbor <- read.csv("~/Senior Year/Senior Design/Texas Neighbors v2.csv", header = T, row.names = 1)
CAneighbor <- read.csv("~/Senior Year/Senior Design/California Neighbors.csv", header = T, row.names = 1)
```

```{r}
cali$DistrictNum <- cali$GEO.id2 - 600
wash$DistrictNum <- wash$GEO.id2 - 5300
penn$DistrictNum <- penn$GEO.id2 - 4200
tx$DistrictNum <- tx$GEO.id2 - 4800
cali2012 <- cali[(1:53),]
wash2012 <- wash[(1:10),]
penn2012 <- penn[(1:18),]
tx2012 <- tx[(1:36),]
```

```{r}
neighbordifference <- function(district, demo, neighbor){ #assumes both are vectors of equal size
  n <- sum(neighbor)
  nbrsdem <- demo[which(neighbor)]
  differences <- abs(demo[district] - nbrsdem)
  neighbordifference <- sum(differences) / n
}

num_districts <- length(cali2012$GEO.id)

agdifferencesCA <- rep(NA, num_districts)
vetdifferencesCA <- rep(NA, num_districts)
blackdifferencesCA <- rep(NA, num_districts)
incomedifferencesCA <- rep(NA, num_districts)
foreigndifferencesCA <- rep(NA, num_districts)
unempdifferencesCA <- rep(NA, num_districts)
youngdifferencesCA <- rep(NA, num_districts)
somecollegedifferencesCA <- rep(NA, num_districts)
whitedifferencesCA <- rep(NA, num_districts)
tworacedifferencesCA <- rep(NA, num_districts)
elderlydifferencesCA <- rep(NA, num_districts)
tradedifferencesCA <- rep(NA, num_districts)
englishdifferencesCA <- rep(NA, num_districts)
marrieddifferencesCA <- rep(NA, num_districts)
hispanicdifferencesCA <- rep(NA, num_districts)
enrolleddifferencesCA <- rep(NA, num_districts)

for (i in 1:num_districts){
  agdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_EC50, unname(CAneighbor[,i]))
  vetdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC101, unname(CAneighbor[,i]))
  blackdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_DH95, unname(CAneighbor[,i]))
  incomedifferencesCA[i] <- neighbordifference(i, cali2012$HC01_EC118, unname(CAneighbor[,i]))
  foreigndifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC140, unname(CAneighbor[,i]))
  unempdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_EC12, unname(CAneighbor[,i]))
  youngdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_EST_ASB02, unname(CAneighbor[,i]))
  somecollegedifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC89, unname(CAneighbor[,i]))
  whitedifferencesCA[i] <- neighbordifference(i, cali2012$HC03_DH94, unname(CAneighbor[,i]))
  tworacedifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC101, unname(CAneighbor[,i]))
  elderlydifferencesCA[i] <- neighbordifference(i, cali2012$HC03_DH102, unname(CAneighbor[,i]))
  tradedifferencesCA[i] <- neighbordifference(i, cali2012$HC03_EC53, unname(CAneighbor[,i]))
  englishdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC171, unname(CAneighbor[,i]))
  marrieddifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC45, unname(CAneighbor[,i]))
  hispanicdifferencesCA[i] <- neighbordifference(i, cali2012$HC03_DH88, unname(CAneighbor[,i]))
  enrolleddifferencesCA[i] <- neighbordifference(i, cali2012$HC03_SC81, unname(CAneighbor[,i]))

}

num_districts <- length(wash2012$DistrictNum)
agdifferencesWA <- rep(NA, num_districts)
vetdifferencesWA <- rep(NA, num_districts)
blackdifferencesWA <- rep(NA, num_districts)
incomedifferencesWA <- rep(NA, num_districts)
foreigndifferencesWA <- rep(NA, num_districts)
unempdifferencesWA <- rep(NA, num_districts)
youngdifferencesWA <- rep(NA, num_districts)
somecollegedifferencesWA <- rep(NA, num_districts)
whitedifferencesWA <- rep(NA, num_districts)
tworacedifferencesWA <- rep(NA, num_districts)
elderlydifferencesWA <- rep(NA, num_districts)
tradedifferencesWA <- rep(NA, num_districts)
englishdifferencesWA <- rep(NA, num_districts)
marrieddifferencesWA <- rep(NA, num_districts)
hispanicdifferencesWA <- rep(NA, num_districts)
enrolleddifferencesWA <- rep(NA, num_districts)

for (i in 1:num_districts){
  agdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_EC50, unname(WAneighbor[,i]))
  vetdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC101, unname(WAneighbor[,i]))
  blackdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_DH95, unname(WAneighbor[,i]))
  incomedifferencesWA[i] <- neighbordifference(i, wash2012$HC01_EC118, unname(WAneighbor[,i]))
  foreigndifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC140, unname(WAneighbor[,i]))
  unempdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_EC12, unname(WAneighbor[,i]))
  youngdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_EST_ASB02, unname(WAneighbor[,i]))
  somecollegedifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC89, unname(WAneighbor[,i]))
  whitedifferencesWA[i] <- neighbordifference(i, wash2012$HC03_DH94, unname(WAneighbor[,i]))
  tworacedifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC101, unname(WAneighbor[,i]))
  elderlydifferencesWA[i] <- neighbordifference(i, wash2012$HC03_DH102, unname(WAneighbor[,i]))
  tradedifferencesWA[i] <- neighbordifference(i, wash2012$HC03_EC53, unname(WAneighbor[,i]))
  englishdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC171, unname(WAneighbor[,i]))
  marrieddifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC45, unname(WAneighbor[,i]))
  hispanicdifferencesWA[i] <- neighbordifference(i, wash2012$HC03_DH88, unname(WAneighbor[,i]))
  enrolleddifferencesWA[i] <- neighbordifference(i, wash2012$HC03_SC81, unname(WAneighbor[,i]))

}

num_districts <- length(penn2012$DistrictNum)
agdifferencesPA <- rep(NA, num_districts)
vetdifferencesPA <- rep(NA, num_districts)
blackdifferencesPA <- rep(NA, num_districts)
incomedifferencesPA <- rep(NA, num_districts)
foreigndifferencesPA <- rep(NA, num_districts)
unempdifferencesPA <- rep(NA, num_districts)
youngdifferencesPA <- rep(NA, num_districts)
somecollegedifferencesPA <- rep(NA, num_districts)
whitedifferencesPA <- rep(NA, num_districts)
tworacedifferencesPA <- rep(NA, num_districts)
elderlydifferencesPA <- rep(NA, num_districts)
tradedifferencesPA <- rep(NA, num_districts)
englishdifferencesPA <- rep(NA, num_districts)
marrieddifferencesPA <- rep(NA, num_districts)
hispanicdifferencesPA <- rep(NA, num_districts)
enrolleddifferencesPA <- rep(NA, num_districts)

for (i in 1:num_districts){
  agdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_EC50, unname(PAneighbor[,i]))
  vetdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC101, unname(PAneighbor[,i]))
  blackdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_DH95, unname(PAneighbor[,i]))
  incomedifferencesPA[i] <- neighbordifference(i, penn2012$HC01_EC118, unname(PAneighbor[,i]))
  foreigndifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC140, unname(PAneighbor[,i]))
  unempdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_EC12, unname(PAneighbor[,i]))
  youngdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_EST_ASB02, unname(PAneighbor[,i]))
  somecollegedifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC89, unname(PAneighbor[,i]))
  whitedifferencesPA[i] <- neighbordifference(i, penn2012$HC03_DH94, unname(PAneighbor[,i]))
  tworacedifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC101, unname(PAneighbor[,i]))
  elderlydifferencesPA[i] <- neighbordifference(i, penn2012$HC03_DH102, unname(PAneighbor[,i]))
  tradedifferencesPA[i] <- neighbordifference(i, penn2012$HC03_EC53, unname(PAneighbor[,i]))
  englishdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC171, unname(PAneighbor[,i]))
  marrieddifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC45, unname(PAneighbor[,i]))
  hispanicdifferencesPA[i] <- neighbordifference(i, penn2012$HC03_DH88, unname(PAneighbor[,i]))
  enrolleddifferencesPA[i] <- neighbordifference(i, penn2012$HC03_SC81, unname(PAneighbor[,i]))
}

num_districts <- length(tx2012$DistrictNum)
agdifferencesTX <- rep(NA, num_districts)
vetdifferencesTX <- rep(NA, num_districts)
blackdifferencesTX <- rep(NA, num_districts)
incomedifferencesTX <- rep(NA, num_districts)
foreigndifferencesTX <- rep(NA, num_districts)
unempdifferencesTX <- rep(NA, num_districts)
youngdifferencesTX <- rep(NA, num_districts)
somecollegedifferencesTX <- rep(NA, num_districts)
whitedifferencesTX <- rep(NA, num_districts)
tworacedifferencesTX <- rep(NA, num_districts)
elderlydifferencesTX <- rep(NA, num_districts)
tradedifferencesTX <- rep(NA, num_districts)
englishdifferencesTX <- rep(NA, num_districts)
marrieddifferencesTX <- rep(NA, num_districts)
hispanicdifferencesTX <- rep(NA, num_districts)
enrolleddifferencesTX <- rep(NA, num_districts)


for (i in 1:num_districts){
  agdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_EC50, unname(TXneighbor[,i]))
  vetdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC101, unname(TXneighbor[,i]))
  blackdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_DH95, unname(TXneighbor[,i]))
  incomedifferencesTX[i] <- neighbordifference(i, tx2012$HC01_EC118, unname(TXneighbor[,i]))
  foreigndifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC140, unname(TXneighbor[,i]))
  unempdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_EC12, unname(TXneighbor[,i]))
  youngdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_EST_ASB02, unname(TXneighbor[,i]))
  somecollegedifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC89, unname(TXneighbor[,i]))
  whitedifferencesTX[i] <- neighbordifference(i, tx2012$HC03_DH94, unname(TXneighbor[,i]))
  tworacedifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC101, unname(TXneighbor[,i]))
  elderlydifferencesTX[i] <- neighbordifference(i, tx2012$HC03_DH102, unname(TXneighbor[,i]))
  tradedifferencesTX[i] <- neighbordifference(i, tx2012$HC03_EC53, unname(TXneighbor[,i]))
  englishdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC171, unname(TXneighbor[,i]))
  marrieddifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC45, unname(TXneighbor[,i]))
  hispanicdifferencesTX[i] <- neighbordifference(i, tx2012$HC03_DH88, unname(TXneighbor[,i]))
  enrolleddifferencesTX[i] <- neighbordifference(i, tx2012$HC03_SC81, unname(TXneighbor[,i]))
}

agconfCA <- t.test(agdifferencesCA, conf.level = 0.95)$conf.int
agconfWA <- t.test(agdifferencesWA, conf.level = 0.95)$conf.int
agconfPA <- t.test(agdifferencesPA, conf.level = 0.95)$conf.int
agconfTX <- t.test(agdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(agdifferencesCA), mean(agdifferencesWA), mean(agdifferencesPA), mean(agdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Percentage of Agriculture Workers", 4)
lowerconf <- c(agconfCA[1], agconfWA[1], agconfPA[1], agconfTX[1])
upperconf <- c(agconfCA[2], agconfWA[2], agconfPA[2], agconfTX[2])
agdf <- data.frame(states, means, lowerconf, upperconf, label)

vetconfCA <- t.test(vetdifferencesCA, conf.level = 0.95)$conf.int
vetconfWA <- t.test(vetdifferencesWA, conf.level = 0.95)$conf.int
vetconfPA <- t.test(vetdifferencesPA, conf.level = 0.95)$conf.int
vetconfTX <- t.test(vetdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(vetdifferencesCA), mean(vetdifferencesWA), mean(vetdifferencesPA), mean(vetdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Percentage of Veterans", 4)
lowerconf <- c(vetconfCA[1], vetconfWA[1], vetconfPA[1], vetconfTX[1])
upperconf <- c(vetconfCA[2], vetconfWA[2], vetconfPA[2], vetconfTX[2])
vetdf <- data.frame(states, means, lowerconf, upperconf, label)

blackconfCA <- t.test(blackdifferencesCA, conf.level = 0.95)$conf.int
blackconfWA <- t.test(blackdifferencesWA, conf.level = 0.95)$conf.int
blackconfPA <- t.test(blackdifferencesPA, conf.level = 0.95)$conf.int
blackconfTX <- t.test(blackdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(blackdifferencesCA), mean(blackdifferencesWA), mean(blackdifferencesPA), mean(blackdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Population Black", 4)
lowerconf <- c(blackconfCA[1], blackconfWA[1], blackconfPA[1], blackconfTX[1])
upperconf <- c(blackconfCA[2], blackconfWA[2], blackconfPA[2], blackconfTX[2])
blackdf <- data.frame(states, means, lowerconf, upperconf, label)

incomeconfCA <- t.test(incomedifferencesCA, conf.level = 0.95)$conf.int
incomeconfWA <- t.test(incomedifferencesWA, conf.level = 0.95)$conf.int
incomeconfPA <- t.test(incomedifferencesPA, conf.level = 0.95)$conf.int
incomeconfTX <- t.test(incomedifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(incomedifferencesCA), mean(incomedifferencesWA), mean(incomedifferencesPA), mean(incomedifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Per Capita Income Difference", 4)
lowerconf <- c(incomeconfCA[1], incomeconfWA[1], incomeconfPA[1], incomeconfTX[1])
upperconf <- c(incomeconfCA[2], incomeconfWA[2], incomeconfPA[2], incomeconfTX[2])
incomedf <- data.frame(states, means, lowerconf, upperconf, label)

foreignconfCA <- t.test(foreigndifferencesCA, conf.level = 0.95)$conf.int
foreignconfWA <- t.test(foreigndifferencesWA, conf.level = 0.95)$conf.int
foreignconfPA <- t.test(foreigndifferencesPA, conf.level = 0.95)$conf.int
foreignconfTX <- t.test(foreigndifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(foreigndifferencesCA), mean(foreigndifferencesWA), mean(foreigndifferencesPA), mean(foreigndifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Foreign Born", 4)
lowerconf <- c(foreignconfCA[1], foreignconfWA[1], foreignconfPA[1], foreignconfTX[1])
upperconf <- c(foreignconfCA[2], foreignconfWA[2], foreignconfPA[2], foreignconfTX[2])
foreigndf <- data.frame(states, means, lowerconf, upperconf, label)

unempconfCA <- t.test(unempdifferencesCA, conf.level = 0.95)$conf.int
unempconfWA <- t.test(unempdifferencesWA, conf.level = 0.95)$conf.int
unempconfPA <- t.test(unempdifferencesPA, conf.level = 0.95)$conf.int
unempconfTX <- t.test(unempdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(unempdifferencesCA), mean(unempdifferencesWA), mean(unempdifferencesPA), mean(unempdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Unemployment Rate", 4)
lowerconf <- c(unempconfCA[1], unempconfWA[1], unempconfPA[1], unempconfTX[1])
upperconf <- c(unempconfCA[2], unempconfWA[2], unempconfPA[2], unempconfTX[2])
unempdf <- data.frame(states, means, lowerconf, upperconf, label)

youngconfCA <- t.test(youngdifferencesCA, conf.level = 0.95)$conf.int
youngconfWA <- t.test(youngdifferencesWA, conf.level = 0.95)$conf.int
youngconfPA <- t.test(youngdifferencesPA, conf.level = 0.95)$conf.int
youngconfTX <- t.test(youngdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(youngdifferencesCA), mean(youngdifferencesWA), mean(youngdifferencesPA), mean(youngdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Aged 20-34", 4)
lowerconf <- c(youngconfCA[1], youngconfWA[1], youngconfPA[1], youngconfTX[1])
upperconf <- c(youngconfCA[2], youngconfWA[2], youngconfPA[2], youngconfTX[2])
youngdf <- data.frame(states, means, lowerconf, upperconf, label)

somecollegeconfCA <- t.test(somecollegedifferencesCA, conf.level = 0.95)$conf.int
somecollegeconfWA <- t.test(somecollegedifferencesWA, conf.level = 0.95)$conf.int
somecollegeconfPA <- t.test(somecollegedifferencesPA, conf.level = 0.95)$conf.int
somecollegeconfTX <- t.test(somecollegedifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(somecollegedifferencesCA), mean(somecollegedifferencesWA), mean(somecollegedifferencesPA), mean(somecollegedifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage with Some College but No Degree", 4)
lowerconf <- c(somecollegeconfCA[1], somecollegeconfWA[1], somecollegeconfPA[1], somecollegeconfTX[1])
upperconf <- c(somecollegeconfCA[2], somecollegeconfWA[2], somecollegeconfPA[2], somecollegeconfTX[2])
somecollegedf <- data.frame(states, means, lowerconf, upperconf, label)

whiteconfCA <- t.test(whitedifferencesCA, conf.level = 0.95)$conf.int
whiteconfWA <- t.test(whitedifferencesWA, conf.level = 0.95)$conf.int
whiteconfPA <- t.test(whitedifferencesPA, conf.level = 0.95)$conf.int
whiteconfTX <- t.test(whitedifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(whitedifferencesCA), mean(whitedifferencesWA), mean(whitedifferencesPA), mean(whitedifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage White", 4)
lowerconf <- c(whiteconfCA[1], whiteconfWA[1], whiteconfPA[1], whiteconfTX[1])
upperconf <- c(whiteconfCA[2], whiteconfWA[2], whiteconfPA[2], whiteconfTX[2])
whitedf <- data.frame(states, means, lowerconf, upperconf, label)

tworaceconfCA <- t.test(tworacedifferencesCA, conf.level = 0.95)$conf.int
tworaceconfWA <- t.test(tworacedifferencesWA, conf.level = 0.95)$conf.int
tworaceconfPA <- t.test(tworacedifferencesPA, conf.level = 0.95)$conf.int
tworaceconfTX <- t.test(tworacedifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(tworacedifferencesCA), mean(tworacedifferencesWA), mean(tworacedifferencesPA), mean(tworacedifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Biracial", 4)
lowerconf <- c(tworaceconfCA[1], tworaceconfWA[1], tworaceconfPA[1], tworaceconfTX[1])
upperconf <- c(tworaceconfCA[2], tworaceconfWA[2], tworaceconfPA[2], tworaceconfTX[2])
tworacedf <- data.frame(states, means, lowerconf, upperconf, label)

elderlyconfCA <- t.test(elderlydifferencesCA, conf.level = 0.95)$conf.int
elderlyconfWA <- t.test(elderlydifferencesWA, conf.level = 0.95)$conf.int
elderlyconfPA <- t.test(elderlydifferencesPA, conf.level = 0.95)$conf.int
elderlyconfTX <- t.test(elderlydifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(elderlydifferencesCA), mean(elderlydifferencesWA), mean(elderlydifferencesPA), mean(elderlydifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage of Elderly Under the Poverty Line", 4)
lowerconf <- c(elderlyconfCA[1], elderlyconfWA[1], elderlyconfPA[1], elderlyconfTX[1])
upperconf <- c(elderlyconfCA[2], elderlyconfWA[2], elderlyconfPA[2], elderlyconfTX[2])
elderlydf <- data.frame(states, means, lowerconf, upperconf, label)

tradeconfCA <- t.test(tradedifferencesCA, conf.level = 0.95)$conf.int
tradeconfWA <- t.test(tradedifferencesWA, conf.level = 0.95)$conf.int
tradeconfPA <- t.test(tradedifferencesPA, conf.level = 0.95)$conf.int
tradeconfTX <- t.test(tradedifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(tradedifferencesCA), mean(tradedifferencesWA), mean(tradedifferencesPA), mean(tradedifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Employed in Wholesale Trade", 4)
lowerconf <- c(tradeconfCA[1], tradeconfWA[1], tradeconfPA[1], tradeconfTX[1])
upperconf <- c(tradeconfCA[2], tradeconfWA[2], tradeconfPA[2], tradeconfTX[2])
tradedf <- data.frame(states, means, lowerconf, upperconf, label)

englishconfCA <- t.test(englishdifferencesCA, conf.level = 0.95)$conf.int
englishconfWA <- t.test(englishdifferencesWA, conf.level = 0.95)$conf.int
englishconfPA <- t.test(englishdifferencesPA, conf.level = 0.95)$conf.int
englishconfTX <- t.test(englishdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(englishdifferencesCA), mean(englishdifferencesWA), mean(englishdifferencesPA), mean(englishdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Percentage Speaking English at Home Difference", 4)
lowerconf <- c(englishconfCA[1], englishconfWA[1], englishconfPA[1], englishconfTX[1])
upperconf <- c(englishconfCA[2], englishconfWA[2], englishconfPA[2], englishconfTX[2])
englishdf <- data.frame(states, means, lowerconf, upperconf, label)

marriedconfCA <- t.test(marrieddifferencesCA, conf.level = 0.95)$conf.int
marriedconfWA <- t.test(marrieddifferencesWA, conf.level = 0.95)$conf.int
marriedconfPA <- t.test(marrieddifferencesPA, conf.level = 0.95)$conf.int
marriedconfTX <- t.test(marrieddifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(marrieddifferencesCA), mean(marrieddifferencesWA), mean(marrieddifferencesPA), mean(marrieddifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Percentage of Married Women Difference", 4)
lowerconf <- c(marriedconfCA[1], marriedconfWA[1], marriedconfPA[1], marriedconfTX[1])
upperconf <- c(marriedconfCA[2], marriedconfWA[2], marriedconfPA[2], marriedconfTX[2])
marrieddf <- data.frame(states, means, lowerconf, upperconf, label)

hispanicconfCA <- t.test(hispanicdifferencesCA, conf.level = 0.95)$conf.int
hispanicconfWA <- t.test(hispanicdifferencesWA, conf.level = 0.95)$conf.int
hispanicconfPA <- t.test(hispanicdifferencesPA, conf.level = 0.95)$conf.int
hispanicconfTX <- t.test(hispanicdifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(hispanicdifferencesCA), mean(hispanicdifferencesWA), mean(hispanicdifferencesPA), mean(hispanicdifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Difference in Percentage Population Hispanic", 4)
lowerconf <- c(hispanicconfCA[1], hispanicconfWA[1], hispanicconfPA[1], hispanicconfTX[1])
upperconf <- c(hispanicconfCA[2], hispanicconfWA[2], hispanicconfPA[2], hispanicconfTX[2])
hispanicdf <- data.frame(states, means, lowerconf, upperconf, label)

enrolledconfCA <- t.test(enrolleddifferencesCA, conf.level = 0.95)$conf.int
enrolledconfWA <- t.test(enrolleddifferencesWA, conf.level = 0.95)$conf.int
enrolledconfPA <- t.test(enrolleddifferencesPA, conf.level = 0.95)$conf.int
enrolledconfTX <- t.test(enrolleddifferencesTX, conf.level = 0.95)$conf.int
means <- c(mean(enrolleddifferencesCA), mean(enrolleddifferencesWA), mean(enrolleddifferencesPA), mean(enrolleddifferencesTX))
states <- c("California", "Washington", "Pennsylvania", "Texas")
label <- rep("Percent Enrolled in College Difference", 4)
lowerconf <- c(enrolledconfCA[1], enrolledconfWA[1], enrolledconfPA[1], enrolledconfTX[1])
upperconf <- c(enrolledconfCA[2], enrolledconfWA[2], enrolledconfPA[2], enrolledconfTX[2])
enrolleddf <- data.frame(states, means, lowerconf, upperconf, label)
```

```{r}
ggplot(vetdf, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1))
```
```{r}
vetag <- rbind(agdf, vetdf)
ggplot(vetag, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1))
```

```{r}
ggplot(agdf, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1))
```

```{r}
ggplot(blackdf, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1))

ggplot(incomedf, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1))
```

```{r}
plot1 <- do.call("rbind", list(blackdf, agdf, vetdf))
plot2 <- do.call("rbind", list(unempdf, youngdf, somecollegedf, whitedf))
plot3 <- do.call("rbind", list(tworacedf, elderlydf, tradedf, englishdf))
plot4 <- do.call("rbind", list(marrieddf, hispanicdf, enrolleddf))
```

```{r}
ggplot(plot1, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
  geom_hline(yintercept = 0)
```

```{r}
ggplot(plot2, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
  geom_hline(yintercept = 0)
```

```{r}
ggplot(plot3, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
  geom_hline(yintercept = 0)
```

```{r}
ggplot(plot4, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
  geom_hline(yintercept = 0)
```

```{r}
englishAllDistricts <- c(englishdifferencesCA, englishdifferencesPA, englishdifferencesTX, englishdifferencesWA)
allStates <- c(rep("California",53), rep("Pennsylvania",18), rep("Texas",36), rep("Washington", 10))
district <- c(cali2012$DistrictNum, penn2012$DistrictNum, tx2012$DistrictNum, wash2012$DistrictNum)
allCommission <- c(rep("Independent", 63), rep("Partisan", 54))
englishBoxDF <- data.frame(englishAllDistricts, allStates, district)

check_outlier <- function(v, coef=1.51){
  quantiles <- quantile(v,probs=c(0.25,0.75))
  IQR <- quantiles[2]-quantiles[1]
  res <- v < (quantiles[1]-coef*IQR)|v > (quantiles[2]+coef*IQR)
  return(res)
}
```

```{r}
library(dplyr)

commissionEnglish <- englishBoxDF %>%
  mutate(commission = ifelse(allStates == "California", "Independent", ifelse(allStates == "Washington", "Independent", "Partisan")))

independentEnglish <- commissionEnglish %>%
  filter(commission == "Independent") %>%
  mutate(outliers = ifelse(check_outlier(englishAllDistricts), paste(allStates, as.character(district), sep = " "), ""))

partisanEnglish <- commissionEnglish %>%
  filter(commission == "Partisan") %>%
  mutate(outliers = ifelse(check_outlier(englishAllDistricts), paste(allStates, as.character(district), sep = " "), ""))

outlierEnglish <- rbind(independentEnglish, partisanEnglish)
```

```{r}
library(ggrepel)
ggplot(data = outlierEnglish, aes(x = commission, y = englishAllDistricts, color = commission)) + 
     geom_boxplot() + ggtitle("Absolute Difference of Percentage Speaking English at Home") +
     theme(plot.title = element_text(hjust = 0.5)) + geom_text_repel(aes(label=outliers))
```
```{r}
plot5 <- do.call("rbind", list(whitedf,blackdf, hispanicdf, tworacedf))
plot6 <- do.call("rbind", list(unempdf, elderlydf, tradedf, agdf))
plot7 <- do.call("rbind", list(enrolleddf, somecollegedf, vetdf))
plot8 <- do.call("rbind", list(youngdf, marrieddf, englishdf))
```

```{r}
ggplot(plot5, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
  geom_hline(yintercept = 0) + ggtitle("Race Differences by State") + ylab("Absolute Difference") + 
  xlab("") + theme(plot.title = element_text(hjust = 0.5))
```
```{r}
ggplot(plot6, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
  geom_hline(yintercept = 0) + ggtitle("Economic Congressional District Neighbor Differences by State") + ylab("Absolute Difference") + 
  xlab("") + theme(plot.title = element_text(hjust = 0.5))
```
```{r}
ggplot(plot7, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
  geom_hline(yintercept = 0) + ggtitle("Educational Congressional District Neighbor Differences by State") + ylab("Absolute Difference") + 
  xlab("") + theme(plot.title = element_text(hjust = 0.5))
```
```{r}
ggplot(plot8, aes(x = label, y = means, color = states)) +
  geom_point(size = 4, shape = 18, position = position_dodge(width=1)) +
  geom_errorbar(aes(ymax = upperconf, ymin = lowerconf), position = position_dodge(width=1)) +
  geom_hline(yintercept = 0) + ggtitle("Social Statistic Congressional District Neighbor Differences by State") + ylab("Absolute Difference") + 
  xlab("") + theme(plot.title = element_text(hjust = 0.5))
```
```{r}
whiteAllDistricts <- c(whitedifferencesCA, whitedifferencesWA, whitedifferencesTX, whitedifferencesPA)
blackAllDistricts <- c(blackdifferencesCA, blackdifferencesWA, blackdifferencesTX, blackdifferencesPA)
hispanicAllDistricts <- c(hispanicdifferencesCA, hispanicdifferencesWA, hispanicdifferencesTX, hispanicdifferencesPA)
tworaceAllDistricts <- c(tworacedifferencesCA, tworacedifferencesWA, tworacedifferencesTX, tworacedifferencesPA)
labels <- c(rep("Percentage White Difference", 117), rep("Percentage Black Difference", 117), rep("Percentage Hispanic Difference", 117),
            rep("Percentage Biracial Difference",117))
raceData <- c(whiteAllDistricts, blackAllDistricts, hispanicAllDistricts, tworaceAllDistricts)
allStates <- rep(c(rep("California",53), rep("Washington",10), rep("Texas",36), rep("Pennsylvania", 18)),4)
allCommission <- rep(c(rep("Independent", 63), rep("Partisan", 54)),4)
district <- rep(c(cali2012$DistrictNum, wash2012$DistrictNum, tx2012$DistrictNum, penn2012$DistrictNum),4)
raceBoxDF <- data.frame(raceData, allStates, allCommission, district, labels)
```

```{r}
unempAllDistricts <- c(unempdifferencesCA, unempdifferencesWA, unempdifferencesTX, unempdifferencesPA)
elderlyAllDistricts <- c(elderlydifferencesCA, elderlydifferencesWA, elderlydifferencesTX, elderlydifferencesPA)
tradeAllDistricts <- c(tradedifferencesCA, tradedifferencesWA, tradedifferencesTX, tradedifferencesPA)
agAllDistricts <- c(agdifferencesCA, agdifferencesWA, agdifferencesTX, agdifferencesPA)
enrolledAllDistricts <- c(enrolleddifferencesCA, enrolleddifferencesWA, enrolleddifferencesTX, enrolleddifferencesPA)
somecollegeAllDistricts <- c(somecollegedifferencesCA, somecollegedifferencesWA, somecollegedifferencesTX, somecollegedifferencesPA)
vetAllDistricts <- c(vetdifferencesCA, vetdifferencesWA, vetdifferencesTX, vetdifferencesPA)
youngAllDistricts <- c(youngdifferencesCA, youngdifferencesWA, youngdifferencesTX, youngdifferencesPA)
marriedAllDistricts <- c(marrieddifferencesCA, marrieddifferencesWA, marrieddifferencesTX, marrieddifferencesPA)
foreignAllDistricts <- c(foreigndifferencesCA, foreigndifferencesWA, foreigndifferencesTX, foreigndifferencesPA)
incomeAllDistricts <- c(incomedifferencesCA, incomedifferencesWA, incomedifferencesTX, incomedifferencesPA)

allData <- c(raceData, unempAllDistricts, elderlyAllDistricts, tradeAllDistricts, agAllDistricts, enrolledAllDistricts, 
             somecollegeAllDistricts, vetAllDistricts, youngAllDistricts, marriedAllDistricts, englishAllDistricts, 
             foreignAllDistricts, incomeAllDistricts)
```

```{r}
labels <- c(rep("Percentage White Difference", 117), 
            rep("Percentage Black Difference", 117), 
            rep("Percentage Hispanic Difference", 117),
            rep("Percentage Biracial Difference",117), 
            rep("Unemployment Rate Difference",117), 
            rep("Percent Poor Elderly Difference", 117),
            rep("Percent Employed in Wholesale Trade Difference", 117), 
            rep("Percent Employed in Agriculture Difference", 117), 
            rep("Percent Enrolled in College Difference", 117), 
            rep("Percent College Dropout Difference", 117), 
            rep("Veteran Population Difference", 117), 
            rep("Percent Age 20-34 Difference",117), 
            rep("Married Female Population Difference", 117),
            rep("Percent Speak English at Home Difference",117),
            rep("Foreign Born Difference",117),
            rep("Per Capita Income Difference", 117))
allStates <- rep(c(rep("California",53), rep("Washington",10), rep("Texas",36), rep("Pennsylvania", 18)),16)
allCommission <- rep(c(rep("Independent", 63), rep("Partisan", 54)),16)
district <- rep(c(cali2012$DistrictNum, wash2012$DistrictNum, tx2012$DistrictNum, penn2012$DistrictNum),16)
allBoxDF <- data.frame(allData, allStates, allCommission, district, labels)
```


```{r}
is_outlier <- function(x) {
  return(x < quantile(x, 0.25) - 1.5 * IQR(x) | x > quantile(x, 0.75) + 1.5 * IQR(x))
}

raceBoxDF %>%
  group_by(labels, allCommission) %>%
  mutate(outlier = ifelse(is_outlier(raceData), paste(allStates, as.character(district), sep = " "), "")) %>%
  ggplot(., aes(x = labels, y = raceData, color = allCommission)) +
    geom_boxplot() + geom_text(aes(label = outlier), position = position_dodge(width=0.25))


```


```{r}
ggplot(raceBoxDF, aes(x = labels, y = raceData, color = allCommission)) +
  geom_boxplot() + ggtitle("Race Differences by State") + ylab("Absolute Difference") + 
  xlab("") + theme(plot.title = element_text(hjust = 0.5))
```
```{r}
allOutlier <- allBoxDF %>%
  group_by(labels, allCommission) %>%
  mutate(outlier = ifelse(is_outlier(allData), paste(allStates, as.character(district), sep = " "), ""))
```

```{r}
ggplot(allOutlier, aes(x = labels, y = allData)) + geom_boxplot(aes(fill = allCommission)) + 
  ggtitle("Demographic Congressional District Differences by District Drawing Method") + ylab("Absolute Difference") + 
  xlab("") + theme(plot.title = element_text(hjust = 0.5)) + #geom_text_repel(aes(label = outlier)) +
  facet_wrap(~ labels, scales = "free")
```

```{r}
ggplot(allOutlier, aes(x = labels, y = allData)) + geom_boxplot(aes(fill = allCommission)) + 
  ggtitle("Demographic Congressional District Differences by District Drawing Method") + ylab("Absolute Difference") + 
  xlab("") + theme(plot.title = element_text(hjust = 0.5)) +
  facet_wrap(~ labels, scales = "free")
```

```{r}
onlyOutliers <- filter(allOutlier, outlier != "")

ggplot(onlyOutliers, aes(allStates)) + geom_bar()
```
```{r}

outlierDistrictCount <- onlyOutliers %>%
  group_by(outlier) %>%
  summarise(n = n()) %>%
  filter(n > 2) %>%
  mutate(state= gsub('[[:digit:]]+', '', outlier))

ggplot(outlierDistrictCount, aes(outlier)) + 
  geom_bar(stat = 'identity', aes(y = n, fill=state)) +
  ggtitle("Number of Variables District is an Outlier On (> 2)") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), plot.title = element_text(hjust = 0.5)) +
  xlab("District") + ylab("Number of Outliers")
  
```
```{r}
totalOutlierCount <- allBoxDF %>%
  group_by(labels, allStates) %>%
  mutate(outlier = ifelse(is_outlier(allData), paste(allStates, as.character(district), sep = " "), "")) %>%
  ungroup() %>%
  filter(outlier != "") %>%
  group_by(outlier) %>%
  summarise(n = n()) %>%
  filter(n > 3) %>%
  mutate(state= gsub('[[:digit:]]+', '', outlier))

ggplot(totalOutlierCount, aes(outlier)) + 
  geom_bar(stat = 'identity', aes(y = n, fill=state)) +
  ggtitle("Number of Variables District is an Outlier On (> 3)") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), plot.title = element_text(hjust = 0.5)) +
  xlab("District") + ylab("Number of Outliers")
```

```{r}
stateOutlierProp <- allBoxDF %>%
  group_by(labels, allCommission) %>%
  mutate(outlier = ifelse(is_outlier(allData), paste(allStates, as.character(district), sep = " "), "")) %>%
  summarise(percent_outlier = ((length(unique(outlier))-1)/n()))

ggplot(stateOutlierProp, aes(allCommission)) + 
  geom_bar(stat = 'identity', aes(y = percent_outlier, fill=allCommission)) +
  ggtitle("Proportion of Districts are Outliers by District Drawing Commission") +
  theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), plot.title = element_text(hjust = 0.5)) +
  xlab("District") + ylab("Number of Outliers") + facet_wrap(~ labels, scales = "free")
```
```{r}
pennOutlierCount <- allOutlier %>%
  filter(allStates == "Pennsylvania") %>%
  group_by(district) %>%
  summarise(times_outlier = sum(ifelse(outlier != "",1,0))) 
```

```{r}
require(tidyverse)
require(ggplot2)
require(sf)
require(ggmap)

get_congress_map <- function(cong=113) {
  tmp_file <- tempfile()
  tmp_dir  <- tempdir()
  zp <- sprintf("http://cdmaps.polisci.ucla.edu/shp/districts%03i.zip",cong)
  download.file(zp, tmp_file)
  unzip(zipfile = tmp_file, exdir = tmp_dir)
  fpath <- paste(tmp_dir, sprintf("districtShapes/districts%03i.shp",cong), sep = "/")
  st_read(fpath)
}
cd114 <- get_congress_map(114)

cd114_pa <- cd114 %>% 
            filter(STATENAME=="Pennsylvania") %>%
            mutate(DISTRICT = as.character(DISTRICT)) %>%
            select(DISTRICT)

outlier_pa <- tibble(DISTRICT=as.character(1:18),
                   Outliers = pennOutlierCount$times_outlier)

cd114_pa <- cd114_pa %>% left_join(outlier_pa, by="DISTRICT")
cd114_pa

```
```{r}
devtools::install_github("tidyverse/ggplot2")
require(ggplot2)
```

```{r}
API_key = 'AIzaSyAcFP4aqbKzzeaBtK9xdyUsX3Lwam9zDAA'
register_google(key = API_key)

dm <- get_map(location="Washington", zoom=6) 
gw <- google_map(key = API_key, zoom = 6)
```


```{r}
map<-get_map(location = c(-77.1945,41.2033), zoom=7)

ggmap(map) + 
  geom_sf(data=cd114_pa,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```
```{r}
washOutlierCount <- allOutlier %>%
  filter(allStates == "Washington") %>%
  group_by(district) %>%
  summarise(times_outlier = sum(ifelse(outlier != "",1,0)))

cd114_wa <- cd114 %>% 
            filter(STATENAME=="Washington") %>%
            mutate(DISTRICT = as.character(DISTRICT)) %>%
            select(DISTRICT)

outlier_wa <- tibble(DISTRICT=as.character(1:10),
                   Outliers = washOutlierCount$times_outlier)

cd114_wa <- cd114_wa %>% left_join(outlier_wa, by="DISTRICT")
```

```{r}
map<-get_map(location = c(-120.7401,47.7511), zoom=6)
```

```{r}
ggmap(map) + 
  geom_sf(data=cd114_wa,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```
```{r}
txOutlierCount <- allOutlier %>%
  filter(allStates == "Texas") %>%
  group_by(district) %>%
  summarise(times_outlier = sum(ifelse(outlier != "",1,0)))

cd114_tx <- cd114 %>% 
            filter(STATENAME=="Texas") %>%
            mutate(DISTRICT = as.character(DISTRICT)) %>%
            select(DISTRICT)

outlier_tx <- tibble(DISTRICT=as.character(1:36),
                   Outliers = txOutlierCount$times_outlier)

cd114_tx <- cd114_tx %>% left_join(outlier_tx, by="DISTRICT")
```

```{r}
map<-get_map(location = c(-99.9018,31.9689), zoom=6)
```
```{r}
ggmap(map) + 
  geom_sf(data=cd114_tx,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```

```{r}
caOutlierCount <- allOutlier %>%
  filter(allStates == "California") %>%
  group_by(district) %>%
  summarise(times_outlier = sum(ifelse(outlier != "",1,0)))

cd114_ca <- cd114 %>% 
            filter(STATENAME=="California") %>%
            mutate(DISTRICT = as.character(DISTRICT)) %>%
            select(DISTRICT)

outlier_ca <- tibble(DISTRICT=as.character(1:53),
                   Outliers = caOutlierCount$times_outlier)

cd114_ca <- cd114_ca %>% left_join(outlier_ca, by="DISTRICT")
```

```{r}
map<-get_map(location = c(-119.4179,36.7783), zoom=6)
```
```{r}
ggmap(map) + 
  geom_sf(data=cd114_ca,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```
```{r}
map_pa<-get_map(location = c(-77.1945,41.2033), zoom=7)
map_wa<-get_map(location = c(-120.7401,47.7511), zoom=6)
map_tx<-get_map(location = c(-99.9018,31.9689), zoom=6)
map_ca<-get_map(location = c(-119.4179,36.7783), zoom=6)
```

```{r}
voteshare_pa <- tibble(DISTRICT=as.character(1:18),
                   `Average D Voteshare` = filter(avg_votes, State == "Pennsylvania")$Average)

cd114_vote_pa <- cd114_pa %>% left_join(voteshare_pa, by="DISTRICT")
```

```{r}
ggmap(map_pa) + 
  geom_sf(data=cd114_vote_pa,aes(fill=`Average D Voteshare`),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "red", high = "blue", limits=c(0,1)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```
```{r}
voteshare_wa <- tibble(DISTRICT=as.character(1:10),
                   `Average D Voteshare` = filter(avg_votes, State == "Washington")$Average)

cd114_vote_wa <- cd114_wa %>% left_join(voteshare_wa, by="DISTRICT")
```

```{r}
ggmap(map_wa) + 
  geom_sf(data=cd114_vote_wa,aes(fill=`Average D Voteshare`),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "red", high = "blue", limits=c(0,1)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```
```{r}
voteshare_ca <- tibble(DISTRICT=as.character(1:53),
                   `Average D Voteshare` = filter(avg_votes, State == "California")$Average)

cd114_vote_ca <- cd114_ca %>% left_join(voteshare_ca, by="DISTRICT")
```

```{r}
ggmap(map_ca) + 
  geom_sf(data=cd114_vote_ca,aes(fill=`Average D Voteshare`),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "red", high = "blue", limits=c(0,1)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```
```{r}
voteshare_tx <- tibble(DISTRICT=as.character(1:36),
                   `Average D Voteshare` = filter(avg_votes, State == "Texas")$Average)

cd114_vote_tx <- cd114_tx %>% left_join(voteshare_tx, by="DISTRICT")
```

```{r}
ggmap(map_tx) + 
  geom_sf(data=cd114_vote_tx,aes(fill=`Average D Voteshare`),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "red", high = "blue", limits=c(0,1)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```

```{r}
map_philly <- get_map(c(-75.1652,39.9526), zoom=9)
```

```{r}
ggmap(map_philly) + 
  geom_sf(data=cd114_pa,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```

```{r}
map_la <- get_map(c(-118.2437,34.0522), zoom=8)
```

```{r}
ggmap(map_la) +
  geom_sf(data=cd114_ca,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```

```{r}
map_dallas <- get_map(c(-96.7970,32.7767), zoom=7)
```

```{r}
ggmap(map_dallas) + 
  geom_sf(data=cd114_tx,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```
```{r}
map_hou <- get_map(c(-95.3698,29.7604), zoom=8)
```

```{r}
ggmap(map_hou) + 
  geom_sf(data=cd114_tx,aes(fill=Outliers),inherit.aes=FALSE,alpha=0.9) + 
  scale_fill_gradient(low = "green", high = "red", limits=c(0,7)) +
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())
```

